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Inductive reasoning sits at the heart of how science actually works—it's the bridge between what we observe and what we conclude. When you're tested on philosophy of science, you're being asked to understand not just that scientists make inferences, but how those inferences are structured, when they're justified, and where they can go wrong. This means grasping the difference between strong and weak inductive arguments, recognizing the role of probability and evidence, and understanding why induction remains philosophically controversial despite its practical success.
The examples below demonstrate core principles: generalization from instances, probabilistic updating, causal discovery, and explanatory inference. Each represents a distinct logical structure with its own standards for good reasoning. Don't just memorize definitions—know what makes each form of induction reliable or unreliable, and be ready to identify which type applies in a given scenario. That's what exam questions will demand.
These methods move from observed instances to broader claims about unobserved cases. The underlying logic assumes that patterns in our sample reflect patterns in the whole—a powerful but risky assumption.
Compare: Enumerative induction vs. generalization—both move from specific to general, but enumerative induction emphasizes counting instances while generalization focuses on extending patterns. FRQ tip: if asked about the "problem of induction," enumerative induction is your clearest example.
These approaches explicitly quantify uncertainty, using mathematical frameworks to express how confident we should be in our conclusions given the evidence.
Compare: Statistical inference vs. Bayesian inference—classical statistics treats probability as long-run frequency, while Bayesianism treats it as degree of belief. This distinction appears frequently in questions about scientific objectivity and the role of background assumptions.
Identifying cause-and-effect relationships requires more than observing correlations. These methods provide systematic procedures for ruling out alternative explanations and isolating genuine causal factors.
Compare: General causal reasoning vs. Mill's methods—causal reasoning is the broad goal, while Mill's methods provide specific procedures for achieving it. Know the method of difference especially well; it underlies experimental design.
These forms of induction don't just generalize from instances—they evaluate which hypothesis best accounts for the evidence or most reliably forecasts future observations.
Compare: Abduction vs. predictive reasoning—abduction looks backward to explain existing evidence, while prediction looks forward to anticipate new observations. Both go beyond the data, but in opposite temporal directions.
These represent induction's role in the creative side of science—generating the claims that will then be tested.
Compare: Hypothesis formation vs. argument from best explanation—hypothesis formation generates candidates, while abduction selects among them. Both are inductive, but they occupy different stages of inquiry.
| Concept | Best Examples |
|---|---|
| Generalization from instances | Enumerative induction, Generalization from specific observations |
| Probabilistic reasoning | Statistical inference, Bayesian inference |
| Causal discovery | Causal reasoning, Mill's methods |
| Explanatory inference | Argument from best explanation (abduction) |
| Forward-looking inference | Predictive reasoning, Scientific hypothesis formation |
| Comparative inference | Analogical reasoning |
| Belief updating | Bayesian inference |
| Systematic methodology | Mill's methods, Statistical inference |
Which two forms of inductive reasoning both involve moving from specific observations to general claims, and what distinguishes their logical structure?
A scientist notices that drug effectiveness varies with dosage and designs an experiment where only dosage changes between groups. Which of Mill's methods is she employing, and why is it considered the strongest?
Compare and contrast Bayesian inference with classical statistical inference—what philosophical disagreement about the nature of probability underlies their different approaches?
If an FRQ asks you to explain how scientists choose between competing theories that equally fit the available data, which form of inductive reasoning should you discuss, and what criteria would you mention?
Why does Hume's problem of induction pose a greater challenge for enumerative induction than for abductive reasoning? What assumption does each form rely on?